**Title: Task-Aligned Self-Supervised Learning for Medical Image Analysis: A Systematic Review and Practical Design Guidelines

Abstract:

Self-supervised learning (SSL) has gained traction as a viable solution to the annotation scarcity inherent in medical imaging, enabling the extraction of meaningful representations from unlabeled datasets. Nevertheless, its success is contingent upon the careful construction of pretext tasks and their congruence with specific clinical goals. This study offers a comprehensive, task-centric review of SSL applications within medical imaging, investigating how various pretext-task configurations impact performance across domains such as classification, segmentation, detection, and other related tasks. Adhering to PRISMA guidelines, we scrutinize 75 publications released between 2017 and 2025, categorizing them into four primary paradigms: contrastive, non-contrastive and predictive, generative and reconstruction-based, and hybrid learning.

Instead of organizing methods strictly by architectural type, we align each paradigm with the downstream objectives it most effectively supports. Our findings indicate that no single SSL strategy is universally superior; rather, performance is determined by the synergy among the pretext task, the imaging modality, and the target task. Specifically, contrastive methods excel at capturing global discriminative features, making them well-suited for classification, though they may fail to detect subtle pathological nuances. Conversely, generative and spatial prediction techniques better retain local anatomical details, rendering them more appropriate for segmentation and dense prediction tasks. Hybrid approaches demonstrate the most balanced results across these metrics.

Furthermore, we emphasize the importance of modality-specific design and highlight that SSL yields the most significant advantages in scenarios characterized by limited labels or few-shot learning conditions. To conclude, we synthesize these insights into actionable design guidelines and identify key open challenges, such as developing pathology-aware pretext tasks, ensuring resource-efficient training for high-dimensional data, and establishing standardized evaluation protocols. This paper aims to provide practical direction for constructing more effective and clinically pertinent SSL frameworks in medical imaging.

Source: arXiv Generated at: 2026-06-02 00:00:00 UTC